The Geothermal Artificial Intelligence for geothermal exploration

Moraga, J.; Duzgun, H. S.; Cavur, M.; Soydan, H.

doi:10.1016/j.renene.2022.04.113

The Geothermal Artificial Intelligence for geothermal exploration

Journal Article · Thu Apr 28 00:00:00 EDT 2022 · Renewable Energy

DOI:https://doi.org/10.1016/j.renene.2022.04.113· OSTI ID:1867408

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Colorado School of Mines, Golden, CO (United States); Colorado School of Mines
Colorado School of Mines, Golden, CO (United States)
Kadir Has Univ., İstanbul (Turkey)

Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. In this paper, we present a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas namely mineral markers, surface temperature, faults and deformation. We demonstrated the implementation of the method in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). Here, we processed various satellite images and geospatial data for mineral markers, temperature, faults and deformation and then implemented ML methods to obtain pattern of surface manifestation of geothermal sites. We developed an AI that uses patterns from surface manifestations to predict geothermal potential of each pixel. We tested the Geothermal AI using independent data sets obtaining accuracy of 92-95%; also tested the Geothermal AI trained on one site by executing it for the other site to predict the geothermal / non-geothermal delineation, the Geothermal AI performed quite well in prediction with 72-76% accuracy.

View Accepted Manuscript (DOE)

Research Organization:: Colorado School of Mines, Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0008760

OSTI ID:: 1867408

Journal Information:: Renewable Energy, Journal Name: Renewable Energy Vol. 192; ISSN 0960-1481

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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